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Answer: Utilize the Databricks Jobs feature to create a single job with multiple notebook tasks and define task dependencies to control the execution sequence.
The Databricks Jobs feature is designed for orchestrating notebook executions in a scalable and automated manner, making it the optimal choice for establishing dependencies between notebooks in a production environment. It allows for the configuration of job dependencies, ensuring that each notebook runs only after its prerequisites have successfully completed. Manual execution (Option A) is error-prone and not scalable. Embedding custom checks (Option C) adds unnecessary complexity and potential points of failure. Using the `%run` magic command (Option D) does not provide the robustness or scalability needed for production workflows.
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In a Databricks environment, you are tasked with orchestrating the execution of multiple notebooks that must run in a specific sequence due to data dependencies. The solution must be scalable, minimize manual intervention, and ensure reliability in a production setting. Considering these requirements, which of the following approaches is the BEST to establish a dependency chain between these notebooks? (Choose one option)
A
Manually execute each notebook in the required order, documenting the sequence for future reference.
B
Utilize the Databricks Jobs feature to create a single job with multiple notebook tasks and define task dependencies to control the execution sequence.
C
Embed a custom script within each notebook that verifies the completion of the preceding notebook before initiating its own execution.
D
Apply the %run magic command in each notebook to call the preceding notebook, creating an implicit execution order.
